Overview

Dataset statistics

Number of variables13
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.7 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with quantity_invoices and 3 other fieldsHigh correlation
recency_days is highly correlated with quantity_invoicesHigh correlation
quantity_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
quantity_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
quantity_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with quantity_products and 1 other fieldsHigh correlation
gross_revenue is highly correlated with quantity_invoices and 1 other fieldsHigh correlation
quantity_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
quantity_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
quantity_products is highly correlated with quantity_invoicesHigh correlation
avg_ticket is highly correlated with quantity_returns and 1 other fieldsHigh correlation
quantity_returns is highly correlated with avg_ticket and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
gross_revenue is highly correlated with quantity_invoices and 2 other fieldsHigh correlation
quantity_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
quantity_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
quantity_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with quantity_itemsHigh correlation
avg_unique_basket_size is highly correlated with quantity_productsHigh correlation
gross_revenue is highly correlated with quantity_invoices and 5 other fieldsHigh correlation
quantity_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
quantity_items is highly correlated with gross_revenue and 5 other fieldsHigh correlation
quantity_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with gross_revenue and 3 other fieldsHigh correlation
quantity_returns is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with quantity_productsHigh correlation
avg_ticket is highly skewed (γ1 = 53.44422362) Skewed
frequency is highly skewed (γ1 = 24.88049136) Skewed
quantity_returns is highly skewed (γ1 = 51.79774426) Skewed
avg_basket_size is highly skewed (γ1 = 44.67271661) Skewed
df_index has unique values Unique
customer_id has unique values Unique
avg_ticket has unique values Unique
recency_days has 34 (1.1%) zeros Zeros
quantity_returns has 1481 (49.9%) zeros Zeros

Reproduction

Analysis started2022-02-12 17:04:27.059896
Analysis finished2022-02-12 17:04:41.798201
Duration14.74 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2317.292354
Minimum0
Maximum5715
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-12T14:04:41.859837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.4
Q1929
median2120
Q33537
95-th percentile5035.2
Maximum5715
Range5715
Interquartile range (IQR)2608

Descriptive statistics

Standard deviation1554.944589
Coefficient of variation (CV)0.6710178739
Kurtosis-1.010787014
Mean2317.292354
Median Absolute Deviation (MAD)1271
Skewness0.342284058
Sum6880041
Variance2417852.674
MonotonicityStrictly increasing
2022-02-12T14:04:41.938960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
26541
 
< 0.1%
26441
 
< 0.1%
5971
 
< 0.1%
26461
 
< 0.1%
5991
 
< 0.1%
26481
 
< 0.1%
6011
 
< 0.1%
6031
 
< 0.1%
51441
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57151
< 0.1%
56961
< 0.1%
56861
< 0.1%
56801
< 0.1%
56591
< 0.1%
56551
< 0.1%
56491
< 0.1%
56381
< 0.1%
56371
< 0.1%
56271
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.77299
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-12T14:04:42.021494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.990292
Coefficient of variation (CV)0.1125673398
Kurtosis-1.206094692
Mean15270.77299
Median Absolute Deviation (MAD)1488
Skewness0.03160785866
Sum45338925
Variance2954927.624
MonotonicityNot monotonic
2022-02-12T14:04:42.101065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163841
 
< 0.1%
181641
 
< 0.1%
129331
 
< 0.1%
129351
 
< 0.1%
149841
 
< 0.1%
170331
 
< 0.1%
137041
 
< 0.1%
129391
 
< 0.1%
170371
 
< 0.1%
141251
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2963
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.321711
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-12T14:04:42.184019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.62331
Coefficient of variation (CV)3.848448607
Kurtosis353.944724
Mean2749.321711
Median Absolute Deviation (MAD)672.16
Skewness16.77755612
Sum8162736.16
Variance111949589.6
MonotonicityNot monotonic
2022-02-12T14:04:42.258853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
379.652
 
0.1%
533.332
 
0.1%
745.062
 
0.1%
734.942
 
0.1%
731.92
 
0.1%
3312
 
0.1%
719.781
 
< 0.1%
13375.871
 
< 0.1%
447.641
 
< 0.1%
567.361
 
< 0.1%
Other values (2953)2953
99.5%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.28763894
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-12T14:04:42.340125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.75677911
Coefficient of variation (CV)1.209513686
Kurtosis2.777962659
Mean64.28763894
Median Absolute Deviation (MAD)26
Skewness1.798379538
Sum190870
Variance6046.116697
MonotonicityNot monotonic
2022-02-12T14:04:42.419191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
285
 
2.9%
385
 
2.9%
876
 
2.6%
1067
 
2.3%
966
 
2.2%
766
 
2.2%
1764
 
2.2%
1655
 
1.9%
Other values (262)2219
74.7%
ValueCountFrequency (%)
034
 
1.1%
199
3.3%
285
2.9%
385
2.9%
487
2.9%
543
1.4%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3724
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

quantity_invoices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.723139104
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-12T14:04:42.503701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.85653132
Coefficient of variation (CV)1.547495379
Kurtosis190.8344494
Mean5.723139104
Median Absolute Deviation (MAD)2
Skewness10.76680458
Sum16992
Variance78.43814702
MonotonicityNot monotonic
2022-02-12T14:04:42.582931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2785
26.4%
3499
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2785
26.4%
3499
16.8%
4393
13.2%
5237
 
8.0%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

quantity_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1671
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1608.852476
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-12T14:04:42.666074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.4
Q1296
median641
Q31401
95-th percentile4407.4
Maximum196844
Range196843
Interquartile range (IQR)1105

Descriptive statistics

Standard deviation5887.578045
Coefficient of variation (CV)3.659489067
Kurtosis465.998084
Mean1608.852476
Median Absolute Deviation (MAD)422
Skewness17.85859125
Sum4776683
Variance34663575.24
MonotonicityNot monotonic
2022-02-12T14:04:42.748684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
889
 
0.3%
1509
 
0.3%
2888
 
0.3%
2728
 
0.3%
848
 
0.3%
2468
 
0.3%
2608
 
0.3%
4937
 
0.2%
1347
 
0.2%
Other values (1661)2886
97.2%
ValueCountFrequency (%)
11
< 0.1%
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%

quantity_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.7241495
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-12T14:04:42.835621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.8964081
Coefficient of variation (CV)2.199211884
Kurtosis354.8611303
Mean122.7241495
Median Absolute Deviation (MAD)44
Skewness15.70763473
Sum364368
Variance72844.07112
MonotonicityNot monotonic
2022-02-12T14:04:42.916160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2843
 
1.4%
2037
 
1.2%
3535
 
1.2%
2935
 
1.2%
1934
 
1.1%
1533
 
1.1%
1132
 
1.1%
2631
 
1.0%
2730
 
1.0%
2530
 
1.0%
Other values (458)2629
88.5%
ValueCountFrequency (%)
16
 
0.2%
214
0.5%
316
0.5%
417
0.6%
526
0.9%
629
1.0%
718
0.6%
819
0.6%
926
0.9%
1028
0.9%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.89776151
Minimum2.150588235
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-12T14:04:42.999880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.916661099
Q113.11933333
median17.95658654
Q324.98828571
95-th percentile90.497
Maximum56157.5
Range56155.34941
Interquartile range (IQR)11.86895238

Descriptive statistics

Standard deviation1036.934407
Coefficient of variation (CV)19.98033011
Kurtosis2890.707126
Mean51.89776151
Median Absolute Deviation (MAD)5.984842033
Skewness53.44422362
Sum154084.4539
Variance1075232.964
MonotonicityNot monotonic
2022-02-12T14:04:43.076653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.492758621
 
< 0.1%
15.413636361
 
< 0.1%
18.150615381
 
< 0.1%
17.943444441
 
< 0.1%
43.21921
 
< 0.1%
33.535714291
 
< 0.1%
9.4182926831
 
< 0.1%
19.557670451
 
< 0.1%
132.07389831
 
< 0.1%
16.807222221
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.34851138
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-12T14:04:43.160062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.92307692
median48.28571429
Q385.33333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.41025641

Descriptive statistics

Standard deviation63.54492876
Coefficient of variation (CV)0.9435238799
Kurtosis4.887109087
Mean67.34851138
Median Absolute Deviation (MAD)26.28571429
Skewness2.062770925
Sum199957.7303
Variance4037.957972
MonotonicityNot monotonic
2022-02-12T14:04:43.239235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
2117
 
0.6%
4617
 
0.6%
1117
 
0.6%
516
 
0.5%
Other values (1248)2777
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
422
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1137973039
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-12T14:04:43.323485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008894164194
Q10.01633986928
median0.02588996764
Q30.04945054945
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.03311068017

Descriptive statistics

Standard deviation0.4081562524
Coefficient of variation (CV)3.586695275
Kurtosis989.3650758
Mean0.1137973039
Median Absolute Deviation (MAD)0.0121913375
Skewness24.88049136
Sum337.8641954
Variance0.1665915263
MonotonicityNot monotonic
2022-02-12T14:04:43.402197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1198
 
6.7%
0.062518
 
0.6%
0.0277777777817
 
0.6%
0.0238095238116
 
0.5%
0.0833333333315
 
0.5%
0.0909090909115
 
0.5%
0.0344827586214
 
0.5%
0.0294117647114
 
0.5%
0.0357142857113
 
0.4%
0.0256410256413
 
0.4%
Other values (1215)2636
88.8%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
26
 
0.2%
1.1428571431
 
< 0.1%
1198
6.7%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%
0.53
 
0.1%

quantity_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.1569552
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-12T14:04:43.487405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.6
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.496135
Coefficient of variation (CV)24.33349783
Kurtosis2765.52864
Mean62.1569552
Median Absolute Deviation (MAD)1
Skewness51.79774426
Sum184544
Variance2287644.557
MonotonicityNot monotonic
2022-02-12T14:04:43.570280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
743
 
1.4%
843
 
1.4%
Other values (204)706
23.8%
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
561
 
2.1%
678
 
2.6%
743
 
1.4%
843
 
1.4%
941
 
1.4%
ValueCountFrequency (%)
809951
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.8137641
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-12T14:04:43.658427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172.3333333
Q3281.6923077
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.4423077

Descriptive statistics

Standard deviation791.5551894
Coefficient of variation (CV)3.168581172
Kurtosis2255.538236
Mean249.8137641
Median Absolute Deviation (MAD)83.08333333
Skewness44.67271661
Sum741697.0657
Variance626559.6179
MonotonicityNot monotonic
2022-02-12T14:04:43.740050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
829
 
0.3%
739
 
0.3%
869
 
0.3%
1368
 
0.3%
758
 
0.3%
888
 
0.3%
608
 
0.3%
1637
 
0.2%
Other values (1969)2882
97.1%
ValueCountFrequency (%)
12
0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1005
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.1547082
Minimum1
Maximum299.7058824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-02-12T14:04:43.826941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.345454545
Q110
median17.2
Q327.75
95-th percentile56.94
Maximum299.7058824
Range298.7058824
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.51232207
Coefficient of variation (CV)0.8807302672
Kurtosis27.70329723
Mean22.1547082
Median Absolute Deviation (MAD)8.2
Skewness3.499455899
Sum65777.32865
Variance380.7307127
MonotonicityNot monotonic
2022-02-12T14:04:43.905886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1353
 
1.8%
1439
 
1.3%
1138
 
1.3%
2033
 
1.1%
933
 
1.1%
132
 
1.1%
1731
 
1.0%
1030
 
1.0%
1830
 
1.0%
1629
 
1.0%
Other values (995)2621
88.3%
ValueCountFrequency (%)
132
1.1%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
0.1%
1.58
 
0.3%
1.5681818181
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
224
0.8%
ValueCountFrequency (%)
299.70588241
< 0.1%
2591
< 0.1%
203.51
< 0.1%
1481
< 0.1%
1451
< 0.1%
136.1251
< 0.1%
135.51
< 0.1%
1271
< 0.1%
1221
< 0.1%
1181
< 0.1%

Interactions

2022-02-12T14:04:40.464818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:28.583443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:29.564421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:30.625001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:31.557161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:32.592357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:33.498946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:34.579967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:35.562841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:36.483856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:37.437326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:38.499406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:39.483987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:40.536117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:28.676831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:29.635262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:30.695598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:31.629184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:32.661072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:33.573322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:34.653900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:35.631787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:36.556192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:37.610593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:38.573986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:39.557590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:40.606388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:28.750380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:29.705320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:30.764752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:31.706520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:32.729440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:33.645976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:34.726184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:35.699668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:36.627526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:37.681267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:38.646392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:39.630892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:40.675400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:28.820378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:29.775892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:30.838581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:31.778029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:32.795450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:33.717603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:34.798430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:35.767601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:36.697420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:37.752115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:38.719883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:39.702603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:40.748795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:28.894326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:29.850619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:30.909993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:31.850894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:32.866038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:33.794900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:34.874356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:35.838221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:36.771250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:37.826246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:38.796645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:39.779070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:40.815283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:28.961095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:30.029372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:30.976686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:31.919074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:32.929296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:33.865864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:34.944016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:35.904829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:36.838487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:37.893747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:38.866526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:39.849338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:40.892059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:29.037716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:30.105368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:31.050693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:32.064837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:33.002589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:33.950398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:35.023179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:35.980266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:36.916029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:37.971918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:38.946047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:39.927761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:40.967383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:29.115308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:30.180809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:31.125469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:32.142193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:33.075435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:34.031337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:35.101637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:36.055748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:36.993118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:38.049650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:39.024211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:40.006730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:41.036591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:29.187096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:30.249796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:31.193568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:32.211024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:33.141464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:34.103212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:35.174092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:36.122556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:37.064226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:38.119931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:39.096467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:40.077702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:41.109597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:29.260732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:30.322230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:31.265241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:32.284622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:33.210666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:34.179623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:35.251320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:36.194249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:37.137629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:38.195974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:39.173310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:40.154866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:41.307148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:29.334918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:30.398815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:31.337900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:32.361134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:33.281298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:34.256377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:35.330432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:36.265204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:37.212241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:38.271110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:39.251097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:40.230897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:41.382608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:29.414610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:30.475298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:31.412364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:32.438561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:33.354903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:34.335415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:35.408569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:36.340262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:37.288461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:38.348677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:39.329633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:40.310566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:41.459374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:29.493006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:30.550952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:31.487094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:32.517959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:33.429115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:34.415296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:35.487270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:36.413353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:37.364769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:38.427070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:39.408415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-12T14:04:40.389454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-12T14:04:43.981211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-12T14:04:44.094431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-12T14:04:44.204618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-12T14:04:44.315616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-12T14:04:41.577910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-12T14:04:41.732036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysquantity_invoicesquantity_itemsquantity_productsavg_ticketavg_recency_daysfrequencyquantity_returnsavg_basket_sizeavg_unique_basket_size
00178505391.21372.034.01733.0297.018.15222235.50000017.00000040.050.9705888.735294
11130473232.5956.09.01390.0171.018.90403527.2500000.02830235.0154.44444419.000000
22125836705.382.015.05028.0232.028.90250023.1875000.04032350.0335.20000015.466667
3313748948.2595.05.0439.028.033.86607192.6666670.0179210.087.8000005.600000
4415100876.00333.03.080.03.0292.0000008.6000000.07317122.026.6666671.000000
55152914623.3025.014.02102.0102.045.32647123.2000000.04011529.0150.1428577.285714
66146885630.877.021.03621.0327.017.21978618.3000000.057221399.0172.42857115.571429
77178095411.9116.012.02057.061.088.71983635.7000000.03352041.0171.4166675.083333
881531160767.900.091.038194.02379.025.5434644.1444440.243316474.0419.71428626.142857
99160982005.6387.07.0613.067.029.93477647.6666670.0243900.087.5714299.571429

Last rows

df_indexcustomer_idgross_revenuerecency_daysquantity_invoicesquantity_itemsquantity_productsavg_ticketavg_recency_daysfrequencyquantity_returnsavg_basket_sizeavg_unique_basket_size
29595627177271060.2515.01.0645.066.016.0643946.01.0000006.0645.00000066.0
2960563717232421.522.02.0203.036.011.70888912.00.1538460.0101.50000018.0
2961563817468137.0010.02.0116.05.027.4000004.00.4000000.058.0000002.5
2962564913596697.045.02.0406.0166.04.1990367.00.2500000.0203.00000083.0
29635655148931237.859.02.0799.073.016.9568492.00.6666670.0399.50000036.5
2964565912479473.2011.01.0382.030.015.7733334.01.00000034.0382.00000030.0
2965568014126706.137.03.0508.015.047.0753333.00.75000050.0169.3333335.0
29665686135211092.391.03.0733.0435.02.5112414.50.3000000.0244.333333145.0
2967569615060301.848.04.0262.0120.02.5153331.02.0000000.065.50000030.0
2968571512558269.967.01.0196.011.024.5418186.01.000000196.0196.00000011.0